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Title: The parametric g-formula for time-to-event data: intuition and a worked example.

Authors: Keil, Alexander P; Edwards, Jessie K; Richardson, David B; Naimi, Ashley I; Cole, Stephen R

Published In Epidemiology, (2014 Nov)

Abstract: The parametric g-formula can be used to estimate the effect of a policy, intervention, or treatment. Unlike standard regression approaches, the parametric g-formula can be used to adjust for time-varying confounders that are affected by prior exposures. To date, there are few published examples in which the method has been applied.We provide a simple introduction to the parametric g-formula and illustrate its application in an analysis of a small cohort study of bone marrow transplant patients in which the effect of treatment on mortality is subject to time-varying confounding.Standard regression adjustment yields a biased estimate of the effect of treatment on mortality relative to the estimate obtained by the g-formula.The g-formula allows estimation of a relevant parameter for public health officials: the change in the hazard of mortality under a hypothetical intervention, such as reduction of exposure to a harmful agent or introduction of a beneficial new treatment. We present a simple approach to implement the parametric g-formula that is sufficiently general to allow easy adaptation to many settings of public health relevance.

PubMed ID: 25140837 Exiting the NIEHS site

MeSH Terms: Adult; Algorithms; Bone Marrow Transplantation/mortality*; Cause of Death; Confounding Factors (Epidemiology); Epidemiologic Methods*; Female; Graft vs Host Disease/epidemiology; Graft vs Host Disease/prevention & control*; Humans; Male; Monte Carlo Method; Multicenter Studies as Topic; Probability

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